Machine Learning For Dummies

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Häftad, Engelska, 2025

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The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can't doLearn the basics of machine learning code and how it integrates with large datasetsUnderstand the mathematical principles that AI uses to make itself smarterConsider real-world applications of machine learning and write your own algorithmsWith clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

Produktinformation

Utgivningsdatum2025-11-20
Mått183 x 226 x 25 mm
Vikt612 g
FormatHäftad
SpråkEngelska
SerieFor Dummies
Antal sidor448
Upplaga3
FörlagJohn Wiley & Sons Inc
ISBN9781394373222

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Introduction 1Part 1: Introducing How Machines Learn 5Chapter 1: Getting the Real Story About AI 7Chapter 2: Learning in the Age of Computers 23Chapter 3: Having a Glance at the Future 35Part 2: Learning Machine Learning by Coding 45Chapter 4: Working with Google Colab 47Chapter 5: Understanding the Tools of the Trade 71Chapter 6: Getting Beyond Basic Coding in Python 81Part 3: Building the Foundations 103Chapter 7: Demystifying the Math Behind Machine Learning 105Chapter 8: Descending the Gradient 129Chapter 9: Validating Machine Learning 145Part 4: Learning from Smart Algorithms 169Chapter 10: Starting with Simple Learners 171Chapter 11: Leveraging Similarity 195Chapter 12: Working with Linear Models the Easy Way 219Chapter 13: Going Beyond the Basics with Support Vector Machines 251Chapter 14: Tackling Complexity with Neural Networks 263Chapter 15: Resorting to Ensembles of Learners 303Part 5: Applying Learning to Real Problems 327Chapter 16: Classifying Images 329Chapter 17: Scoring Opinions and Sentiments 351Chapter 18: Recommending Products and Movies 379Part 6: The Part of Tens 401Chapter 19: Ten Ways to Improve Your Machine Learning Models 403Chapter 20: Ten Guidelines for Ethical Data Usage 411Index 419